UC Irvine

Medical image segmentation is the process of segmenting/ sectioning out a particular structure of interest from an entire image, which is obtained from an imaging modality such as MRI or CT. The segmentation procedure used often depends on different factors such as the imaging modality, the properties of the structure of interest and the computational performance required. The problem of image segmentation is a widely explored topic in the domain of medical image processing. It makes the study of complex structures easier, which in turn helps immensely in better diagnosis and treatment planning. In this work, the aim is to study the performance of five different approaches for segmenting five different structures of the human brain in a T1 MR image. These methods make use of information from already segmented reference images to perform segmentation on the input and hence are classified as multi-atlas (multiple references) based techniques. They treat the entire brain volume as a group of patches (made of individual voxels) and perform segmentation by operating at the patch level and hence are called the patch based methods. The Dice coefficient is used as a measure to evaluate segmentation performance by each of these methods. Through this analysis, the objective is to implement, understand and analyze each of these methods and also identify their shortcomings.